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Remote Rlhf Jobs in Fairfield, CA (NOW HIRING)

High Volume (TOFU) Recruiter

San Francisco, CA ยท On-site +1

$55K - $100K/yr

San Francisco, CA preferred; open to other remote options About the Role HumanSignal Services runs ... Familiarity with AI data operations, annotation, or RLHF workforce programs * Experience with ATS ...

Delivery Lead

San Francisco, CA ยท Remote

$110K - $140K/yr

... and remote workforce marketplaces can't. We own projects end-to-end, from scoping and protocol ... Our work spans RLHF, evals, red-teaming, and custom multimodal data creation, all powered by Label ...

Strategic Projects Lead

San Francisco, CA ยท Remote

$75K - $110K/yr

... and remote workforce marketplaces can't. We own projects end-to-end, from scoping and protocol ... Our work spans RLHF, evals, red-teaming, and custom multimodal data creation, all powered by Label ...

Senior ML Engineer

San Francisco, CA ยท On-site +1

$123K - $169K/yr

Familiarity with RLHF or preference training is a bonus ๐Ÿ“ Location This is a remote-first role. We are currently hiring in the following locations: ๐Ÿ“ United States: Greater Los Angeles Area ...

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Remote Rlhf information

How does a Remote RLHF (Reinforcement Learning from Human Feedback) specialist typically collaborate with other team members?

A Remote RLHF specialist often works closely with data scientists, machine learning engineers, and product managers to design and refine AI models using human feedback. Collaboration usually happens through regular virtual meetings, cloud-based code repositories, and shared annotation tools. The role requires clear communication to ensure that human feedback is accurately integrated into the learning process and that model improvements align with project goals. Being proactive in sharing findings and challenges is key, as team members may be distributed across different time zones.

What is the difference between Remote Rlhf vs Remote Rlhf?

AspectRemote RlhfRemote Rlhf
CredentialsTypically requires certification in mental health or counseling, such as LPC or LCSWSimilar credentials, often with additional training in specific therapy methods
Work EnvironmentRemote, client-facing sessions via telehealth platformsRemote, providing therapy or support services online
Industry UsageCommon in mental health, therapy, and counseling sectorsUsed in mental health and support services, often interchangeably with Rlhf

Remote Rlhf and Remote Rlhf are similar roles in mental health support, primarily differing in specific certifications or training focus. Both roles involve providing remote therapy or support services via telehealth platforms, making them highly comparable in work environment and industry usage.

What are the key skills and qualifications needed to thrive as a Remote RLHF (Reinforcement Learning from Human Feedback) Engineer, and why are they important?

To succeed as a Remote RLHF Engineer, you need expertise in machine learning, reinforcement learning, and programming languages like Python, often supported by an advanced degree in computer science or related fields. Familiarity with ML frameworks (such as TensorFlow or PyTorch), version control systems, and cloud computing platforms is typically required. Strong problem-solving, communication, and self-management skills are vital for remote collaboration and interpreting human feedback effectively. These skills enable the development of robust AI systems that can learn efficiently from human input while ensuring productive teamwork in a distributed environment.

What is a Remote RLHF job?

A Remote RLHF (Reinforcement Learning from Human Feedback) job involves working with artificial intelligence systems, particularly large language models, to improve their performance using feedback from humans. In this role, individuals may annotate data, provide quality evaluations, or help design feedback mechanisms while working from a remote location. These jobs are crucial for ensuring AI models align better with human values and expectations, and they are often offered by AI research companies or organizations focused on machine learning. The work can involve tasks such as ranking AI-generated responses, identifying errors, and suggesting improvements. Remote RLHF positions are popular due to their flexibility and the opportunity to contribute to cutting-edge AI technology.
What are popular job titles related to Remote Rlhf jobs in Fairfield, CA? For Remote Rlhf jobs in Fairfield, CA, the most frequently searched job titles are:
What job categories do people searching Remote Rlhf jobs in Fairfield, CA look for? The top searched job categories for Remote Rlhf jobs in Fairfield, CA are:
What cities near Fairfield, CA are hiring for Remote Rlhf jobs? Cities near Fairfield, CA with the most Remote Rlhf job openings:
Infographic showing various Remote Rlhf job openings in Fairfield, CA as of June 2026, with employment types broken down into 78% Full Time, 11% Part Time, and 11% Contract. Highlights an 100% Remote job distribution.

Forward Deployed Engineer (Inference & Post-Training)

Together AI

San Francisco, CA โ€ข On-site, Remote

$270K - $300K/yr

Other

Medical

Posted 20 days ago


Job description

About the role

As a Forward Deployed Engineer (FDE) focused on Inference & Post-Training, you will be a hands-on technical partner to our most strategic customers - production AI teams looking to leverage high quality models and do inference at scale. For us, FDE is not a replacement for a Solutions Architect; you will partner with our SAs as a deep-domain specialist in inference optimization, fine-tuning pipelines, and production deployment. As key contributors to both the CX, Engineering, and Sales organizations, FDEs add tremendous value by ensuring we can meet the requirements of our most complex POCs, facilitate successful platform adoption, and guide tailored optimization efforts - directly impacting customer success, company growth, and the hardening of our core platform.

Responsibilities
  • Inference Engine Optimization: Select, configure, and optimize inference engine based on hardware, model architecture, and workload profile
  • Configuration & Performance Tuning: Develop configuration updates to win critical POCs, benchmarks, and optimize customer deployments; tune KV cache, apply speculative decoding, determine optimal tensor parallelism, and determine quantization strategy to hit throughput and latency targets.
  • Post-Training & Fine-Tuning: Drive hands-on RL training runs and optimize system design; guide customers through LoRA, SFT, DPO, RLHF, and GRPO pipelines from experimentation through production.
  • Strategic Customer Alignment: Act as the primary technical point of contact for aligned strategic accounts - monitoring and optimizing endpoint configurations, helping customers get the most out of the platform, and collaborating to ensure we hit critical milestones.
  • Opinionated Onboarding: Establish direct alignment with strategic customers at onboarding; ensure the right inference and post-training configurations are in place from day one to improve time-to-value.
  • Product Feedback Loop: Directly influence our software and model roadmap by surfacing insights from the field. Contribute back to the product where needed to support customer requirements or drive a better experience. Drive early feature and research adoption with strategic logos.
Qualifications
  • Experience: 5+ years in a technical role, with a strong focus on inference systems, open-source LLM deployment, or post-training workflows.
  • Inference Engine Depth: Expert-level, hands-on experience with inference engines (e.g., vLLM, TensorRT-LLM, SGLang); ability to diagnose and resolve performance issues at the engine level.
  • Inference Optimization: Deep knowledge of KV cache tuning, speculative decoding, tensor parallelism, pipeline parallelism, and quantization techniques
  • Post-Training Knowledge: Hands-on experience with fine-tuning and post-training pipelines, including LoRA, SFT, DPO, RLHF, and GRPO; ability to advise on system design
  • Model Landscape Awareness: Broad knowledge of state-of-the-art open-source models and strong judgment on model selection for specific customer use cases, hardware profiles, and performance targets.
  • Coding Proficiency: Strong Python skills; comfortable working in production environments
About Together AI

Together AI is a research-driven artificial intelligence company. We believe open and transparent AI systems will drive innovation and create the best outcomes for society, and together we are on a mission to significantly lower the cost of modern AI systems by co-designing software, hardware, algorithms, and models. We have contributed to leading open-source research, models, and datasets to advance the frontier of AI, and our team has been behind technological advancements such as FlashAttention, Hyena, FlexGen, and RedPajama. We invite you to join a passionate group of researchers on our journey in building the next generation of AI infrastructure.ย 

Compensation

We offer competitive compensation, startup equity, health insurance, and other benefits, as well as flexibility in terms of remote work. The US base salary range for this full-time position is: $270,000 - $300,000 OTE + equity + benefits. Our salary ranges are determined by location, level and role. Individual compensation will be determined by experience, skills, and job-related knowledge.ย 

Equal Opportunity

Together AI is an Equal Opportunity Employer and is proud to offer equal employment opportunity to everyone regardless of race, color, ancestry, religion, sex, national origin, sexual orientation, age, citizenship, marital status, disability, gender identity, veteran status, and more.

Please see our Privacy Policy at https://www.together.ai/privacy